US8665333B1 - Method and system for optimizing the observation and annotation of complex human behavior from video sources - Google Patents
Method and system for optimizing the observation and annotation of complex human behavior from video sources Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
- G08B13/19602—Image analysis to detect motion of the intruder, e.g. by frame subtraction
- G08B13/19613—Recognition of a predetermined image pattern or behaviour pattern indicating theft or intrusion
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
- G08B13/19639—Details of the system layout
- G08B13/19641—Multiple cameras having overlapping views on a single scene
- G08B13/19643—Multiple cameras having overlapping views on a single scene wherein the cameras play different roles, e.g. different resolution, different camera type, master-slave camera
Definitions
- the present invention is a method and system for automatically detecting predefined events based on the behavior of people in a first video stream from a first means for capturing images in a physical space, accessing a synchronized second video stream from a second means for capturing images that are positioned to observe the people more closely using the timestamps associated with the detected events from the first video stream, and enabling an annotator to annotate each of the events with more labels using an annotation tool.
- Trajkovic U.S. Pat. Appl. Pub. No. 2003/0058339 of Trajkovic, et al. (hereinafter Trajkovic) disclosed a method for detecting an event through repetitive patterns of human behavior. Trajkovic learned multi-dimensional feature data from the repetitive patterns of human behavior and computed a probability density function (PDF) from the data. Then, a method for the PDF analysis, such as Gaussian or clustering techniques, was used to identify the repetitive patterns of behavior and unusual behavior through the variance of the Gaussian distribution or cluster.
- PDF probability density function
- Trajkovic can model a repetitive behavior through the PDF analysis, Trajkovic are clearly foreign to the event detection for the aggregate of non-repetitive behaviors, such as the shopper traffic in a physical space. Trajkovic did not disclose the challenges in the event detection based on customers' behaviors in a video in a retail environment, such as the non-repetitive behaviors. Therefore, Trajkovic are clearly foreign to the challenges that can be found in a retail environment.
- Bazakos disclosed a method for unsupervised learning of events in a video.
- Bazakos disclosed a method of creating a feature vector of a related object in a video by grouping clusters of points together within a feature space and storing the feature vector in an event library. Then, the behavioral analysis engine in Bazakos determined whether an event had occurred by comparing features contained within a feature vector in a specific instance against the feature vectors in the event library.
- Bazakos are primarily related to surveillance, rather than event detection based on customers' behaviors in a video.
- U.S. Pat. Appl. Pub. No. 2005/0286774 of Porikli disclosed a method for event detection in a video using approximate estimates of the aggregated affinity matrix and clustering and scoring of the matrix.
- Porikli constructed the affinity matrix based on a set of frame-based and object-based statistical features, such as trajectories, histograms, and Hidden Markov Models of feature speed, orientation, location, size, and aspect ratio, extracted from the video.
- the step of receiving the user input via input devices makes Sorensen 1 inefficient for handling a large amount of video data in a large shopping environment with a relatively complicated store layout, especially over a long period of time.
- the manual input by a human operator/user cannot efficiently track all of the shoppers in such cases, partially due to the possibility of human errors caused by tiredness and boredom.
- the manual input approach is also much less scalable as the number of shopping environments to handle for the behavior analysis increases. Therefore, an automated event detection approach is needed.
- the present invention utilizes an automated event detection approach for detecting predefined events from the customers' shopping interaction in a physical space.
- Sorensen 2 Although U.S. Pat. Appl. Pub. No. 2002/0178085 of Sorensen, now U.S. Pat. No. 7,006,982, (hereinafter Sorensen 2) disclosed a usage of a tracking device and store sensors in a plurality of tracking systems primarily based on the wireless technology, such as the RFID, Sorensen 2 is clearly foreign to the concept of applying computer vision based tracking algorithms to the field of understanding customers' shopping behaviors and movements. In Sorensen 2, each transmitter was typically attached to a hand-held or push-type cart. Therefore, Sorensen 2 cannot distinguish the behaviors of multiple shoppers using one cart from the behavior of a single shopper also using one cart.
- Sorensen 2 disclosed that the transmitter may be attached directly to a shopper, via a clip or other form of customer surrogate in order to correctly track the shopper in the case when the person is shopping without a cart, this will not be practical due to the additionally introduced cumbersome step to the shopper, not to mention the inefficiency of managing the transmitter for each individual shopper.
- the present invention can embrace any type of automatic wireless sensors for the detection of the predefined events. However, in a preferred embodiment, the present invention primarily utilizes the computer vision based automated approach for the detection of the predefined events. The computer vision based event detection helps the present invention to overcome the obstacles mentioned above.
- Steenburgh disclosed a relevant exemplary prior art.
- Steenburgh disclosed a method for measuring dwell time of an object, particularly a customer in a retail store, which enters and exits an environment, by tracking the object and matching the entry signature of the object to the exit signature of the object, in order to find out how long people spend in retail stores.
- the modeling and analysis of activity of interest can be used as the exemplary way to detect predefined events.
- Choi a method for modeling an activity of a human body using optical flow vector from a video and probability distribution of the feature vectors from the optical flow vector. Choi modeled a plurality of states using the probability distribution of the feature vectors and expressed the activity based on the state transition.
- Pavlidis U.S. Pat. Appl. Pub. No. 2003/0053659 of Pavlidis, et al. (hereinafter Pavlidis) disclosed a method for moving object assessment, including an object path of one or more moving objects in a search area, using a plurality of imaging devices and segmentation by background subtraction.
- object included customers
- Pavlidis also included itinerary statistics of customers in a department store.
- Pavlidis was primarily related to monitoring a search area for surveillance.
- U.S. Pat. Appl. Pub. No. 2004/0113933 of Guler disclosed a method for automatic detection of split and merge events from video streams in a surveillance environment.
- Guler considered split and merge behaviors as key common simple behavior components in order to analyze high level activities of interest in a surveillance application, which are also used to understand the relationships among multiple objects not just individual behavior.
- Guler used adaptive background subtraction to detect the objects in a video scene, and the objects were tracked to identify the split and merge behaviors.
- To understand the split and merge behavior-based high level events Guler used a Hidden Markov Model (HMM).
- HMM Hidden Markov Model
- Ozer U.S. Pat. Appl. Pub. No. 2004/0120581 of Ozer, et al.
- a method for identifying activity of customers for a marketing purpose or activity of objects in a surveillance area by comparing the detected objects with the graphs from a database.
- Ozer tracked the movement of different object parts and combined them to high-level activity semantics, using several Hidden Markov Models (HMMs) and a distance classifier.
- HMMs Hidden Markov Models
- Dove U.S. Pat. No. 6,741,973 of Dove, et al. (hereinafter Dove) disclosed a model of generating customer behavior in a transaction environment.
- Dove disclosed video cameras in a real bank branch as a way to observe the human behavior
- Dove are clearly foreign to the concept of automatic event detection based on the customers' behaviors on visual information of the customers in other types of physical space, such as the shopping path tracking and analysis in a retail environment, for the sake of annotating the customers' behaviors.
- Computer vision algorithms have been shown to be an effective means for detecting and tracking people. These algorithms also have been shown to be effective in analyzing the behavior of people in the view of the means for capturing images. This allows the possibility of connecting the visual information from a scene to the behavior analysis of customers and predefined event detection.
- the present invention provides a novel approach for annotating the customers' behaviors utilizing the information from the automatic behavior analysis of customers and predefined event detection. Any reliable automatic behavior analysis in the prior art may be used for the predefined event detection in the present invention.
- Computer vision algorithms have been shown to be an effective means for analyzing the demographic information of people in the view of the means for capturing images.
- recognizing the demographic category of a person by processing the facial image using various approaches in the computer vision technologies, such as a machine learning approach.
- Moghaddam U.S. Pat. No. 6,990,217 of Moghaddam, et al. (hereinafter Moghaddam) disclosed a method to employ Support Vector Machine to classify images of faces according to gender by training the images, including images of male and female faces; determining a plurality of support vectors from the training images for identifying a hyperplane for the gender decision; and reducing the resolution of the training images and the test image by sub-sampling before supplying the images to the Support Vector Machine.
- U.S. Pat. Appl. Pub. No. 20030110038 of Sharma, et al. disclosed a computer software system for multi-modal human gender classification, comprising: a first-mode classifier classifying first-mode data pertaining to male and female subjects according to gender, and rendering a first-mode gender-decision for each male and female subject; a second-mode classifier classifying second-mode data pertaining to male and female subjects according to gender, and rendering a second-mode gender-decision for each male and female subject; and a fusion classifier integrating the individual gender decisions obtained from said first-mode classifier and said second-mode classifier, and outputting a joint gender decision for each of said male and female subjects.
- the face tracking algorithm has been designed and tuned to improve the classification accuracy; the facial geometry correction step improves both the tracking and the individual face classification accuracy, and the tracking further improves the accuracy of the classification of gender and ethnicity over the course of visibly tracked faces by combining the individual face classification scores.
- the present invention detects the predefined events based on the demographic information of people in another exemplary embodiment.
- the invention automatically and unobtrusively analyzes the customers' demographic information without involving any hassle to customers or operators of feeding the information manually, utilizing the novel demographic analysis approaches in the prior arts.
- the present invention utilizes the event detection by the automatic behavior analysis and demographic analysis in a first video stream to synchronize the same event in another second video stream and allows an annotator to annotate the synchronized event through an annotation tool.
- the manual annotation data in the present invention can be used for various market analysis applications, such as measuring deeper insights for customers' shopping behavior analysis in a retail store, media effectiveness measurement, and traffic analysis.
- the present invention is a method and system for optimizing the observation and annotation of predefined events by enabling the automatic detection of predefined events based on the behavior of people in a first video stream from a first means for capturing images in a physical space and the annotation for each of the events by an annotator utilizing an annotation tool.
- the present invention captures a plurality of input images of the persons by a plurality of first means for capturing images and processes the plurality of input images in order to detect the predefined events based on the behavior analysis of the people in an exemplary embodiment.
- Utilization of the dwell time of the people in a specific location of the physical space can be used as one of the exemplary criteria for defining the targeted behavior. Examples of the temporal targeted behavior can comprise passerby behavior and engaged shopper behavior based on the dwell time measurement and comparison against predefined thresholds.
- the processes are based on a novel usage of a plurality of computer vision technologies to analyze the human behavior from the plurality of input images.
- the method leverages the strengths of the technologies in the present invention and processes to deliver a new level of access to the behaviors and visual characteristics of people in the physical space.
- the automatic event detection in the present invention can also be triggered by the other visual characteristics and segmentation of people in the physical space, such as the demographics, in another exemplary embodiment. Therefore, it is another objective of the present invention to process the first video stream in order to detect demographics of the people in the field of view of the first means for capturing images automatically and generate time-stamped lists of events based on the automatically detected demographics of the people for the predefined event detection.
- An exemplary embodiment of the present invention can be applied to a retail space application, and it can provide demographic segmentation of the shoppers by gender and age group in this particular application domain.
- the shopping behavior of each demographic group can be analyzed to obtain segment-specific insights. Understanding segment-based shopper behavior for a specific business goal in the retail space can help to develop effective customer-centric strategies to increase the basket size and loyalty of the highest-opportunity segments.
- the present invention utilizes a plurality of first means for capturing images and a plurality of second means for capturing images in a preferred embodiment.
- the first means for capturing images can be an overhead top-down camera
- the second means for capturing images can be a camera that is positioned to observe the people more closely for analyzing a specific event.
- the present invention can also utilize different types of sensors for the automatic event detection.
- the present invention can utilize a wireless sensor based tracking for the automatic event detection or a door sensor to trigger an event.
- the wireless sensor can include, but are not limited to, a RFID and means for using the RFID.
- the present invention generates time-stamped lists of events based on the automatically detected predefined events. Then, it can access a synchronized second video stream from a second means for capturing images that are positioned to observe the people more closely using the timestamps associated with the detected events from the first video stream. Using the timestamps and the time-stamped lists of events, the present invention can access the corresponding sub-streams for the events in the synchronized second video stream.
- a time-server can be used in order to maintain a synchronized time in the network of means for control and processing in the present invention.
- the present invention can enable an annotator to manually annotate each of the synchronized events in the corresponding sub-streams for the events in the synchronized second video stream, with a plurality of labels, using a tool.
- the annotation tool can comprise a user interface for the annotation.
- Examples of the user interface can comprise a digital annotation tool or an analog annotation tool.
- the user interface allows users to mark time-based annotations describing more complex behavioral issues, which may not be detected by using a fully automated method and require human identification. Examples of the more complex behavioral issues can comprise expressions of the people.
- the tool can further comprise a graphical user interface for the annotation to further make the analysis more efficient.
- the graphical user interface can be used to browse the video streams based on the timestamps of the events, such as the beginning and end time.
- the physical space may be a retail space, and the people may be customers or shoppers in the retail space in the description of the invention.
- the solution in the present invention can help the owner of the particular embodiment to have in-depth understanding of shopper behavior.
- the annotation can be utilized for more quantitative and deeper behavior analysis about the interaction of people with commercial products in the retail space.
- the present invention can also generate statistical reports by aggregating the annotated events.
- the disclosed method may be described in the context of a retail space, the present invention can be applied to any physical space, and the application area of the present invention is not limited to the retail space.
- the present invention can utilize a rule-based logic module for the synchronization between the first video stream and the second video stream. This enables dynamic rule application, where the synchronization can be adjusted based on the rules defined in the module, rather than the synchronization relying on an ad-hoc solution or static hard-code.
- FIG. 1 is an overview of a preferred embodiment of the invention, where the present invention detects predefined events in a first video stream from a top-down first means for capturing images and generates time-stamped lists of events, which are used to access the corresponding sub-streams for the events in a synchronized second video stream from a second means for capturing images for the annotation of the events.
- FIG. 2 is an overview of another exemplary embodiment of the invention, where the present invention uses a different type of sensor for detecting the predefined events.
- FIG. 3 shows an exemplary scene of the annotation process by an annotator for the synchronized view of the events, using an exemplary annotation tool.
- FIG. 4 shows an exemplary annotation tool in the present invention.
- FIG. 5 shows an exemplary synchronization architecture in an exemplary network of a plurality of means for control and processing in the present invention, where the network consists of a plurality of first means for control and processing and a plurality of second means for control and processing, which communicate with each other to synchronize the time-stamped lists of events among a plurality of video streams for the detected events.
- FIG. 6 shows overall processes of an exemplary embodiment of the present invention, comprising the automatic event detection in a first video stream, the synchronization of the event in a corresponding second video stream, and the annotation of the detected event in the synchronized second video stream.
- FIG. 7 shows detailed exemplary processes of predefined event detection, based on the behavior analysis of the people, in an exemplary automatic event detection module in the present invention.
- FIG. 8 shows detailed exemplary processes of automatic detection of predefined events in another exemplary embodiment of the present invention, where the predefined event detection also uses the segmentation information of the people, such as demographics, in an exemplary automatic event detection module.
- FIG. 1 is an overview of a preferred embodiment of the invention, where the present invention detects predefined events in a first video stream from a top-down first means for capturing images 101 and generates time-stamped lists of events, which are used to access the corresponding sub-streams for the events in a synchronized second video stream from a second means for capturing images 102 for the annotation of the events.
- the processes in the present invention are based on a novel usage of a plurality of computer vision technologies to analyze the human behavior from the plurality of input images.
- the method leverages the strengths of the technologies in the present invention and processes to deliver a new level of access to the behaviors and visual characteristics of people in the physical space.
- the present invention captures a plurality of input images of the people in a physical space 130 by a plurality of first means for capturing images 101 and processes the plurality of input images in order to detect the predefined events based on the behavior analysis of the people in the physical space.
- the behavior analysis and the following automatic event detection can be based on the spatial and temporal attributes of the person tracking in the field of view of a first means for capturing images 101 .
- an exemplary “event detection 1” 251 can comprise the automatically measured spatial and temporal attributes about the detected event, such as the time “Ti” when the event occurred and the location “(Xi, Yi)” of the event, the assigned event identification “EID1”, and the event type “ET1” of the specific event.
- the utilization of the dwell time of the people in a specific location of the physical space can be used as one of the criteria for defining the targeted behavior.
- Examples of the temporal targeted behavior can comprise passerby behavior and engaged shopper behavior, based on the dwell time measurement and comparison against predefined thresholds.
- the present invention can utilize a plurality of first means for capturing images 101 and a plurality of second means for capturing images 102 in a preferred embodiment.
- the first means for capturing images 101 can be an overhead top-down camera
- the second means for capturing images 102 can be a camera that is positioned to observe the people more closely for analyzing a specific event.
- the present invention generates time-stamped lists of events based on the automatically detected predefined events. Then, it can access a synchronized second video stream from a second means for capturing images 102 that is positioned to observe the people more closely, using the timestamps associated with the detected events from the first video stream. Using the timestamps and the time-stamped lists of events, the present invention can access the corresponding sub-streams for the events in the synchronized second video stream.
- the physical space may be a retail space, and the people may be customers or shoppers in the retail space in the description of the invention.
- the solution in the present invention can help the owner of the particular embodiment to have an in-depth understanding of shopper behavior.
- the annotation can be utilized for more quantitative and deeper behavior analysis about the interaction of people with commercial products in the retail space.
- the present invention can also generate statistical reports by aggregating the annotated events.
- the disclosed method may be described in the context of a retail space, the present invention can be applied to any physical space, and the application area of the present invention is not limited to the retail space.
- FIG. 2 is an overview of another exemplary embodiment of the invention, where the present invention uses a different type of sensor for detecting the predefined events.
- the automatic behavior analysis of people is the preferred method for detecting the predefined event in the present invention.
- the automatic event detection can also be triggered by the other visual characteristics and segmentation of people in the physical space, such as the demographics, in another exemplary embodiment.
- the present invention can process the first video stream in order to detect the demographics of the people in the field of view of the first means for capturing images automatically and generate time-stamped lists of events based on the automatically detected demographics of the people for the predefined event detection.
- an exemplary “event detection 2” 252 can comprise the automatically measured spatial and temporal attributes about the detected event, such as the time “Tj” when the event occurred and the location “(Xj, Yj)” of the event, the assigned event identification “EID2”, and the event type “ET2” of the specific event.
- another exemplary “event detection 3” 253 can comprise the automatically measured spatial and temporal attributes about the detected event, such as the time “Tk” when the event occurred and the location “(Xk, Yk)” of the event, the assigned event identification “EID3”, and the event type “ET3” of the specific event.
- the event types can be defined in association with the automatic demographic measurement, respectively.
- the present invention can provide demographic segmentation of the shoppers by gender and age group in this particular application domain.
- the shopping behavior of each demographic group can be analyzed to obtain segment-specific insights. Understanding segment-based shopper behavior for a specific business goal in the retail space can help to develop effective customer-centric strategies to increase the basket size and loyalty of the highest-opportunity segments.
- the present invention can utilize a plurality of first means for capturing images 101 and a plurality of second means for capturing images 102 in a preferred embodiment.
- the first means for capturing images 101 can be an overhead top-down camera
- the second means for capturing images 102 can be a camera that is positioned to observe the people more closely for analyzing a specific event.
- the present invention can also utilize different types of sensors for a different type of automatic event detection, such as a wireless sensor, a door sensor 116 , or other types of sensors in an electronic article surveillance (EAS) system.
- a wireless sensor can include, but are not limited to, a RFID and means for using the RFID.
- a sequence of the RFID proximity detection can be used to provide tracking information of the people.
- the present invention can use a door sensor 116 to trigger a different type of event, such as an anti-theft alarm event.
- FIG. 3 shows an exemplary scene of the annotation 280 process by an annotator for the synchronized view of the events using an exemplary annotation tool 160 .
- the present invention can enable an annotator to manually annotate each of the synchronized events in the corresponding sub-streams for the events in the synchronized second video stream 172 , with a plurality of labels, using an annotation tool 160 .
- the annotation tool 160 can comprise a user interface for the annotation.
- Examples of the user interface can comprise a digital annotation tool or an analog annotation tool.
- the user interface allows users to mark time-based annotations describing more complex behavioral issues, which may not be detected by using a fully automated method and require human identification. Examples of the more complex behavioral issues can comprise expressions of the people.
- the present invention detects an exemplary event, “event detection 1 ” 251 , in a first video stream 171 , and then the annotator can use the annotation tool 160 to find the corresponding synchronized event in a second video stream 172 , utilizing the attributes in the exemplary “event detection 1 ” 251 .
- the annotator can also use the annotation tool 160 to watch and annotate the synchronized event in a second video stream 172 by accessing the synchronized view of the event 265 in the annotation tool 160 .
- the present invention can also display the top-down event detection view from the first video stream 171 on a means for playing output 103 .
- FIG. 4 shows an exemplary annotation tool 160 in the present invention.
- the annotation tool 160 can further comprise a graphical user interface 162 for the annotation to further make the analysis more efficient as shown in FIG. 4 .
- the graphical user interface 162 can be used to browse the video streams based on the timestamps of the events, such as the beginning and end time.
- the exemplary graphical user interface 162 can comprise event selection 176 , video stream selection 177 , event timeline selection 178 , and other facilitating interface capabilities.
- the annotator can browse through time-stamped lists of events, automatically generated by the present invention, and select a synchronized second video stream among a plurality of available second video streams, using the video stream selection 177 . After a second video stream, relevant to the target event for annotation, is selected, the annotator can quickly and efficiently access the corresponding sub-streams for the event in the synchronized second video stream, using the timestamps for the detected events.
- FIG. 5 shows an exemplary synchronization architecture in an exemplary network of a plurality of means for control and processing in the present invention, where the network consists of a plurality of first means for control and processing 107 and a plurality of second means for control and processing 108 , which communicate with each other to synchronize the time-stamped lists of events among a plurality of video streams for the detected events.
- the present invention generates time-stamped lists of events based on the automatically detected predefined events. Then, it can access a synchronized second video stream from a second means for capturing images that are positioned to observe the people more closely, using the timestamps associated with the detected events from the first video stream. Using the timestamps and the time-stamped lists of events, the present invention can access the corresponding sub-streams for the events in the synchronized second video stream.
- the utilization of the automatic event detection and the synchronization efficiently help the annotation process by reducing the amount of video streams and the time to handle and by allowing the annotator to focus more on the interested events according to the predefined rules for the automatically detected events.
- a time-server 109 can be used in order to maintain a synchronized time in the network of means for control and processing in the present invention.
- the exemplary network of a plurality of means for control and processing can consist of a plurality of first means for control and processing 107 and a plurality of second means for control and processing 108 .
- a first means for control and processing 107 can act as a server and a plurality of second means for control and processing 108 can act as clients.
- the server can run its own local clock or be connected to a global time-server 109 for the synchronization utilizing a time synchronization protocol, such as the Network Time Protocol (NTP).
- NTP Network Time Protocol
- the number of means for capturing images per a means for control and processing varies, depending on the system configuration in the physical space.
- each means for control and processing knows the location and the identification of each of its associated plurality of means for capturing images and the area covered by the means for capturing images. Therefore, when an event is detected by a top-down first means for capturing images 101 at a location, its associated first means for control and processing 107 can correctly find the corresponding second means for capturing images 102 close to the specific location, through communicating with the second means for control and processing, associated with the corresponding second means for capturing images 102 .
- the present invention when an event is detected by the “first means for capturing images at location L1” 110 , the present invention can correctly find the corresponding event and sub-streams from the “second means for capturing images at location L1” 112 . Likewise, the present invention can correlate the events between the “first means for capturing images at location Ln” 111 and the “second means for capturing images at location Ln” 113 for the location Ln, using their location and identification information.
- the present invention can utilize a rule-based logic module for the synchronization among a plurality of the first video streams and a plurality of the second video streams.
- a rule-based logic module for the synchronization among a plurality of the first video streams and a plurality of the second video streams.
- the annotator can select and utilize any of the plurality of the second video streams from their associated second means for capturing images.
- the rule-based logic module can also further help the annotator by providing more information about the detected event and synchronization, based on the predefined rules in the module.
- the logic module can provide priority information among the plurality of second video streams according to the predefined rules for the order, relevance, and specific needs at the specific location in the physical space.
- the rule-based logic module can also enable a dynamic rule application, where the synchronization can be adjusted dynamically based on the rules defined in the module, rather than the synchronization relying on an ad-hoc solution or static hard-code.
- FIG. 6 shows overall processes of an exemplary embodiment of the present invention, comprising the automatic event detection 255 in a first video stream 171 , the synchronization 260 of the event in a corresponding second video stream, and the annotation 280 of the detected event in the synchronized second video stream.
- the present invention processes a generation of lists of events 256 , based on the “automatic event detection” 255 in a first video stream 171 , from a first means for capturing images 101 .
- an annotator can use the information in the generated events, such as the timestamp, the location of the corresponding second means for capturing images and the corresponding second means for control and processing, and their identifications, to find 272 and access 273 a synchronized second video stream, among a plurality of available second video streams, i.e. “second video stream 1” 173 , “second video stream 2” 174 , and “second video stream N” 175 , from the corresponding second means for capturing images that are positioned to observe the people more closely, utilizing an annotation tool.
- the annotator further uses the detailed information for the target event, such as the start and end timestamps of the event, to access the relevant sub-streams in the synchronized second video stream for the final annotation 280 of the specific event, based on the domain specific parameters 282 .
- FIG. 7 shows detailed exemplary processes of predefined event detection based on the behavior analysis of the people in an exemplary “automatic event detection” 255 module in the present invention.
- the present invention detects 710 and tracks 714 a person in a physical space for the path analysis 470 , and the information in the path analysis 470 , such as the sequence of coordinates and temporal attributes, are used for the behavior analysis 480 of the person.
- the present invention can utilize any reliable video-based tracking method for people in the prior art in regards to the behavior analysis.
- U.S. Pat. No. 7,974,869 of Sharma, et al. (hereinafter Sharma869) disclosed an exemplary process of video-based tracking and behavior analysis for a single customer or a group of customers using multiple means for capturing images, based on the spatial and temporal attributes of the person tracking.
- FIG. 20 and FIG. 21 in Sharma869 show exemplary spatio-temporal primitives for modeling human-object behavior and exemplary shopping interaction levels that are observed to produce the behavioral analysis in a physical space.
- the behavior recognition can be achieved via spatio-temporal analysis of tracks, using geometry and pattern recognition techniques.
- the approach for defining and detecting spatio-temporal relations specific to the retail enterprise domain followed by a Bayesian Belief propagation approach to modeling primitive behaviors specific to the retail domain, as an exemplary site of a media network in Sharma869, can be applied to any physical space.
- the exemplary primitive behaviors comprised categories of “customer moves towards object”, “customer doesn't walk towards object”, “customer velocity reduces”, “customer velocity increases”, “customer stands in front of object”, and “customer walks away from object”, and these primitive behaviors were combined to model predefined complex behaviors. Then the behaviors of the people were analyzed based on the model. Walkthrough history, the time spent in a certain area within a physical space, frequency pattern, relational pattern, and special event pattern can also be used as the exemplary attributes for the behavior analysis.
- the exemplary shopping interaction levels in Sharma869 can be regarded as an exemplary higher level of complex behaviors in a target physical space, especially in a retail space, which are observed to produce the behavioral analysis in the context of the present invention.
- Sharma869 defined the exemplary shopping interaction levels based on the spatio-temporal relations, which are “passing by”, “noticing”, “stopping”, from “engaging 1” to “engaging P-1”, and “purchase”. They are labeled as “level 1” interaction, “level 2” interaction, “level 3” interaction, from “level 4” interaction to “level P-1” interaction, and “level p” interaction, respectively, where multiple engaging levels are also considered.
- the shopping interaction level can be measured based on the temporal attribute of the person tracking for the customer in regards to the combination of the primitive behaviors. For example, if there is no change in velocity, the present invention can measure the customer's interaction level as a passer-by level at a particular category. If the stopping time T1 is greater than a threshold, such as T1 seconds, then the present invention can measure the customer's interaction level as a level 4 interaction. Likewise, the temporal attribute of the person tracking can match the time value to the corresponding interaction levels, based on the predefined threshold and rules.
- the present invention can detect 250 the predefined events and generate a list of the detected events 256 .
- FIG. 8 shows detailed exemplary processes of automatic detection of predefined events in another exemplary embodiment of the present invention, where the predefined event detection also uses the segmentation information of the people, such as demographics, in an exemplary automatic event detection module.
- the present invention can process the event detection 250 based on the behavior analysis of the people in a physical space and generate a list of detected events 256 as described in regards to FIG. 7 .
- the computer vision based automatic segmentation 241 of the people on a video can also be used as one of the criteria to define certain types of events.
- Automatic demographic classification 814 can be used as an exemplary segmentation of the people.
- the present invention can process segmentation 241 of the customer, such as the demographic classification 814 , based on the images of the people in a first video stream 171 and use the segmentation 241 information to detect the predefined events based on the segmentation criteria.
- the present invention can utilize any reliable demographic composition measurement method in the prior art as an exemplary video-based segmentation of the customers.
- any reliable demographic composition measurement method in the prior art as an exemplary video-based segmentation of the customers.
- U.S. Provisional Pat. No. 60/808,283 of Sharma, et al. disclosed an exemplary demographic composition measurement based on gender and ethnicity.
- Age is also another attribute that Sharma 60/808,283 can measure.
- Automatic event detection based on the segmentation of the people in a physical space can provide unique benefits to the annotator and the owner of a particular embodiment of the present invention.
- the detailed annotation labels can be efficiently organized based on the predefined segmentation criteria in the events.
- Detailed annotation labels per demographic groups can be a very useful market analysis data in an exemplary embodiment of the present invention.
Abstract
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---|---|---|---|---|
US20120120201A1 (en) * | 2010-07-26 | 2012-05-17 | Matthew Ward | Method of integrating ad hoc camera networks in interactive mesh systems |
US20130036011A1 (en) * | 2011-08-01 | 2013-02-07 | Verizon Patent And Licensing, Inc. | Targeted Advertisement Content Presentation Methods and Systems |
US20130050496A1 (en) * | 2011-08-25 | 2013-02-28 | Electronics & Telecommunications Research Institute | Security monitoring method and apparatus using augmented reality |
US20130159460A1 (en) * | 2011-12-16 | 2013-06-20 | Mindshare Networks, Inc. | Harnessing naturally occurring characteristics of social networks |
US20130311230A1 (en) * | 2012-05-17 | 2013-11-21 | Catalina Marketing Corporation | System and method of initiating in-trip audits in a self-checkout system |
US20140063262A1 (en) * | 2012-08-31 | 2014-03-06 | Ncr Corporation | Techniques for checkout security using video surveillance |
US20140198216A1 (en) * | 2011-09-22 | 2014-07-17 | Koninklijke Philips N.V. | Imaging service using outdoor lighting networks |
US20140313413A1 (en) * | 2011-12-19 | 2014-10-23 | Nec Corporation | Time synchronization information computation device, time synchronization information computation method and time synchronization information computation program |
US20140363049A1 (en) * | 2011-12-21 | 2014-12-11 | Universite Pierre Et Marie Curie (Paris 6) | Method of estimating optical flow on the basis of an asynchronous light sensor |
US20150356062A1 (en) * | 2014-06-06 | 2015-12-10 | International Business Machines Corporation | Indexing and annotating a usability test recording |
US20160078286A1 (en) * | 2013-04-26 | 2016-03-17 | Nec Corporation | Monitoring device, monitoring method and monitoring program |
US20160078302A1 (en) * | 2014-09-11 | 2016-03-17 | Iomniscient Pty Ltd. | Image management system |
US9508239B1 (en) | 2013-12-06 | 2016-11-29 | SkyBell Technologies, Inc. | Doorbell package detection systems and methods |
US9736284B2 (en) | 2013-07-26 | 2017-08-15 | SkyBell Technologies, Inc. | Doorbell communication and electrical systems |
US9743049B2 (en) | 2013-12-06 | 2017-08-22 | SkyBell Technologies, Inc. | Doorbell communication systems and methods |
US20170241185A1 (en) * | 2008-07-18 | 2017-08-24 | Robert Osann, Jr. | Moving door system synchronized with pedestrians passing there-through |
US9769435B2 (en) | 2014-08-11 | 2017-09-19 | SkyBell Technologies, Inc. | Monitoring systems and methods |
US9997036B2 (en) | 2015-02-17 | 2018-06-12 | SkyBell Technologies, Inc. | Power outlet cameras |
US10044519B2 (en) | 2015-01-05 | 2018-08-07 | SkyBell Technologies, Inc. | Doorbell communication systems and methods |
JP2018534826A (en) * | 2015-09-23 | 2018-11-22 | ノキア テクノロジーズ オーユー | Select video content |
US10204467B2 (en) | 2013-07-26 | 2019-02-12 | SkyBell Technologies, Inc. | Smart lock systems and methods |
US10262331B1 (en) | 2016-01-29 | 2019-04-16 | Videomining Corporation | Cross-channel in-store shopper behavior analysis |
US10354262B1 (en) | 2016-06-02 | 2019-07-16 | Videomining Corporation | Brand-switching analysis using longitudinal tracking of at-shelf shopper behavior |
EP3311334A4 (en) * | 2015-06-18 | 2019-08-07 | Wizr | Cloud platform with multi camera synchronization |
US10387896B1 (en) | 2016-04-27 | 2019-08-20 | Videomining Corporation | At-shelf brand strength tracking and decision analytics |
EP3405889A4 (en) * | 2016-01-21 | 2019-08-28 | Wizr LLC | Cloud platform with multi camera synchronization |
US10462097B2 (en) * | 2013-12-16 | 2019-10-29 | Inbubbles Inc. | Space time region based communications |
US10489660B2 (en) | 2016-01-21 | 2019-11-26 | Wizr Llc | Video processing with object identification |
US10572843B2 (en) * | 2014-02-14 | 2020-02-25 | Bby Solutions, Inc. | Wireless customer and labor management optimization in retail settings |
US10665072B1 (en) * | 2013-11-12 | 2020-05-26 | Kuna Systems Corporation | Sensor to characterize the behavior of a visitor or a notable event |
US10733823B2 (en) | 2013-07-26 | 2020-08-04 | Skybell Technologies Ip, Llc | Garage door communication systems and methods |
US10909825B2 (en) | 2017-09-18 | 2021-02-02 | Skybell Technologies Ip, Llc | Outdoor security systems and methods |
US10922555B1 (en) * | 2019-10-25 | 2021-02-16 | 7-Eleven, Inc. | Customer-based video feed |
US10963893B1 (en) | 2016-02-23 | 2021-03-30 | Videomining Corporation | Personalized decision tree based on in-store behavior analysis |
US11017229B2 (en) | 2019-10-25 | 2021-05-25 | 7-Eleven, Inc. | System and method for selectively verifying algorithmically populated shopping carts |
US11023728B1 (en) | 2019-10-25 | 2021-06-01 | 7-Eleven, Inc. | Machine learning algorithm trained to identify algorithmically populated shopping carts as candidates for verification |
US11074790B2 (en) | 2019-08-24 | 2021-07-27 | Skybell Technologies Ip, Llc | Doorbell communication systems and methods |
US11087271B1 (en) | 2017-03-27 | 2021-08-10 | Amazon Technologies, Inc. | Identifying user-item interactions in an automated facility |
US11102027B2 (en) | 2013-07-26 | 2021-08-24 | Skybell Technologies Ip, Llc | Doorbell communication systems and methods |
US11132877B2 (en) | 2013-07-26 | 2021-09-28 | Skybell Technologies Ip, Llc | Doorbell communities |
US11140253B2 (en) | 2013-07-26 | 2021-10-05 | Skybell Technologies Ip, Llc | Doorbell communication and electrical systems |
US11184589B2 (en) | 2014-06-23 | 2021-11-23 | Skybell Technologies Ip, Llc | Doorbell communication systems and methods |
US11210531B2 (en) * | 2018-08-20 | 2021-12-28 | Canon Kabushiki Kaisha | Information processing apparatus for presenting location to be observed, and method of the same |
US11228739B2 (en) | 2015-03-07 | 2022-01-18 | Skybell Technologies Ip, Llc | Garage door communication systems and methods |
US11238401B1 (en) | 2017-03-27 | 2022-02-01 | Amazon Technologies, Inc. | Identifying user-item interactions in an automated facility |
US11295135B2 (en) * | 2020-05-29 | 2022-04-05 | Corning Research & Development Corporation | Asset tracking of communication equipment via mixed reality based labeling |
US11326387B2 (en) * | 2008-07-18 | 2022-05-10 | Robert Osann, Jr. | Automatic access control devices and clusters thereof |
US11343473B2 (en) | 2014-06-23 | 2022-05-24 | Skybell Technologies Ip, Llc | Doorbell communication systems and methods |
US11354683B1 (en) | 2015-12-30 | 2022-06-07 | Videomining Corporation | Method and system for creating anonymous shopper panel using multi-modal sensor fusion |
US11361641B2 (en) | 2016-01-27 | 2022-06-14 | Skybell Technologies Ip, Llc | Doorbell package detection systems and methods |
US11374808B2 (en) | 2020-05-29 | 2022-06-28 | Corning Research & Development Corporation | Automated logging of patching operations via mixed reality based labeling |
US11380091B2 (en) | 2019-10-25 | 2022-07-05 | 7-Eleven, Inc. | System and method for populating a virtual shopping cart based on a verification of algorithmic determinations of items selected during a shopping session in a physical store |
US11381686B2 (en) | 2015-04-13 | 2022-07-05 | Skybell Technologies Ip, Llc | Power outlet cameras |
US11386730B2 (en) | 2013-07-26 | 2022-07-12 | Skybell Technologies Ip, Llc | Smart lock systems and methods |
US11386647B2 (en) | 2019-10-25 | 2022-07-12 | 7-Eleven, Inc. | System and method for processing a refund request arising from a shopping session in a cashierless store |
US20220264053A1 (en) * | 2019-10-30 | 2022-08-18 | Beijing Bytedance Network Technology Co., Ltd. | Video processing method and device, terminal, and storage medium |
US11494729B1 (en) * | 2017-03-27 | 2022-11-08 | Amazon Technologies, Inc. | Identifying user-item interactions in an automated facility |
US11575537B2 (en) | 2015-03-27 | 2023-02-07 | Skybell Technologies Ip, Llc | Doorbell communication systems and methods |
US11615430B1 (en) * | 2014-02-05 | 2023-03-28 | Videomining Corporation | Method and system for measuring in-store location effectiveness based on shopper response and behavior analysis |
US20230103735A1 (en) * | 2021-10-05 | 2023-04-06 | Motorola Solutions, Inc. | Method, system and computer program product for reducing learning time for a newly installed camera |
US11651668B2 (en) | 2017-10-20 | 2023-05-16 | Skybell Technologies Ip, Llc | Doorbell communities |
US11651665B2 (en) | 2013-07-26 | 2023-05-16 | Skybell Technologies Ip, Llc | Doorbell communities |
US11889009B2 (en) | 2013-07-26 | 2024-01-30 | Skybell Technologies Ip, Llc | Doorbell communication and electrical systems |
Citations (38)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5600368A (en) * | 1994-11-09 | 1997-02-04 | Microsoft Corporation | Interactive television system and method for viewer control of multiple camera viewpoints in broadcast programming |
US5745036A (en) * | 1996-09-12 | 1998-04-28 | Checkpoint Systems, Inc. | Electronic article security system for store which uses intelligent security tags and transaction data |
US20010049826A1 (en) * | 2000-01-19 | 2001-12-06 | Itzhak Wilf | Method of searching video channels by content |
US20020085092A1 (en) | 2000-11-14 | 2002-07-04 | Samsung Electronics Co., Ltd. | Object activity modeling method |
US6430357B1 (en) * | 1998-09-22 | 2002-08-06 | Ati International Srl | Text data extraction system for interleaved video data streams |
US20020161804A1 (en) * | 2001-04-26 | 2002-10-31 | Patrick Chiu | Internet-based system for multimedia meeting minutes |
US20020178085A1 (en) * | 2001-05-15 | 2002-11-28 | Herb Sorensen | Purchase selection behavior analysis system and method |
US20030002712A1 (en) * | 2001-07-02 | 2003-01-02 | Malcolm Steenburgh | Method and apparatus for measuring dwell time of objects in an environment |
US6531963B1 (en) * | 2000-01-18 | 2003-03-11 | Jan Bengtsson | Method for monitoring the movements of individuals in and around buildings, rooms and the like |
US20030053659A1 (en) * | 2001-06-29 | 2003-03-20 | Honeywell International Inc. | Moving object assessment system and method |
US20030058339A1 (en) | 2001-09-27 | 2003-03-27 | Koninklijke Philips Electronics N.V. | Method and apparatus for detecting an event based on patterns of behavior |
US20030108223A1 (en) * | 1998-10-22 | 2003-06-12 | Prokoski Francine J. | Method and apparatus for aligning and comparing images of the face and body from different imagers |
US20030110038A1 (en) | 2001-10-16 | 2003-06-12 | Rajeev Sharma | Multi-modal gender classification using support vector machines (SVMs) |
US6597391B2 (en) * | 1997-09-17 | 2003-07-22 | Sony United Kingdom Limited | Security system |
US20040032495A1 (en) * | 2000-10-26 | 2004-02-19 | Ortiz Luis M. | Providing multiple synchronized camera views for broadcast from a live venue activity to remote viewers |
US20040078809A1 (en) * | 2000-05-19 | 2004-04-22 | Jonathan Drazin | Targeted advertising system |
US6741973B1 (en) | 1997-04-04 | 2004-05-25 | Ncr Corporation | Consumer model |
US20040113933A1 (en) | 2002-10-08 | 2004-06-17 | Northrop Grumman Corporation | Split and merge behavior analysis and understanding using Hidden Markov Models |
US20040120581A1 (en) | 2002-08-27 | 2004-06-24 | Ozer I. Burak | Method and apparatus for automated video activity analysis |
US20040131254A1 (en) * | 2000-11-24 | 2004-07-08 | Yiqing Liang | System and method for object identification and behavior characterization using video analysis |
US20040161133A1 (en) * | 2002-02-06 | 2004-08-19 | Avishai Elazar | System and method for video content analysis-based detection, surveillance and alarm management |
US20050286774A1 (en) | 2004-06-28 | 2005-12-29 | Porikli Fatih M | Usual event detection in a video using object and frame features |
US20060010030A1 (en) * | 2004-07-09 | 2006-01-12 | Sorensen Associates Inc | System and method for modeling shopping behavior |
US20060010028A1 (en) * | 2003-11-14 | 2006-01-12 | Herb Sorensen | Video shopper tracking system and method |
US6990217B1 (en) * | 1999-11-22 | 2006-01-24 | Mitsubishi Electric Research Labs. Inc. | Gender classification with support vector machines |
US20060023073A1 (en) * | 2004-07-27 | 2006-02-02 | Microsoft Corporation | System and method for interactive multi-view video |
US20060047674A1 (en) * | 2004-09-01 | 2006-03-02 | Mohammed Zubair Visharam | Method and apparatus for supporting storage of multiple camera views |
US20060053342A1 (en) | 2004-09-09 | 2006-03-09 | Bazakos Michael E | Unsupervised learning of events in a video sequence |
WO2006106496A1 (en) * | 2005-04-03 | 2006-10-12 | Nice Systems Ltd. | Apparatus and methods for the semi-automatic tracking and examining of an object or an event in a monitored site |
US20060239645A1 (en) * | 2005-03-31 | 2006-10-26 | Honeywell International Inc. | Event packaged video sequence |
US20070055563A1 (en) * | 2000-08-29 | 2007-03-08 | Godsey Ronald G | System and methods for tracking consumers in a store environment |
US20070208263A1 (en) * | 2006-03-01 | 2007-09-06 | Michael Sasha John | Systems and methods of medical monitoring according to patient state |
US20070250901A1 (en) * | 2006-03-30 | 2007-10-25 | Mcintire John P | Method and apparatus for annotating media streams |
US7536706B1 (en) * | 1998-08-24 | 2009-05-19 | Sharp Laboratories Of America, Inc. | Information enhanced audio video encoding system |
US7623755B2 (en) * | 2006-08-17 | 2009-11-24 | Adobe Systems Incorporated | Techniques for positioning audio and video clips |
US20100002082A1 (en) * | 2005-03-25 | 2010-01-07 | Buehler Christopher J | Intelligent camera selection and object tracking |
US20110261172A1 (en) * | 2008-04-17 | 2011-10-27 | Terry Robert L | Stereoscopic viewer |
US8295597B1 (en) * | 2007-03-14 | 2012-10-23 | Videomining Corporation | Method and system for segmenting people in a physical space based on automatic behavior analysis |
-
2008
- 2008-01-25 US US12/011,385 patent/US8665333B1/en active Active
Patent Citations (40)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5600368A (en) * | 1994-11-09 | 1997-02-04 | Microsoft Corporation | Interactive television system and method for viewer control of multiple camera viewpoints in broadcast programming |
US5745036A (en) * | 1996-09-12 | 1998-04-28 | Checkpoint Systems, Inc. | Electronic article security system for store which uses intelligent security tags and transaction data |
US6741973B1 (en) | 1997-04-04 | 2004-05-25 | Ncr Corporation | Consumer model |
US6597391B2 (en) * | 1997-09-17 | 2003-07-22 | Sony United Kingdom Limited | Security system |
US7536706B1 (en) * | 1998-08-24 | 2009-05-19 | Sharp Laboratories Of America, Inc. | Information enhanced audio video encoding system |
US6430357B1 (en) * | 1998-09-22 | 2002-08-06 | Ati International Srl | Text data extraction system for interleaved video data streams |
US20030108223A1 (en) * | 1998-10-22 | 2003-06-12 | Prokoski Francine J. | Method and apparatus for aligning and comparing images of the face and body from different imagers |
US6990217B1 (en) * | 1999-11-22 | 2006-01-24 | Mitsubishi Electric Research Labs. Inc. | Gender classification with support vector machines |
US6531963B1 (en) * | 2000-01-18 | 2003-03-11 | Jan Bengtsson | Method for monitoring the movements of individuals in and around buildings, rooms and the like |
US20010049826A1 (en) * | 2000-01-19 | 2001-12-06 | Itzhak Wilf | Method of searching video channels by content |
US20040078809A1 (en) * | 2000-05-19 | 2004-04-22 | Jonathan Drazin | Targeted advertising system |
US20070055563A1 (en) * | 2000-08-29 | 2007-03-08 | Godsey Ronald G | System and methods for tracking consumers in a store environment |
US7796162B2 (en) * | 2000-10-26 | 2010-09-14 | Front Row Technologies, Llc | Providing multiple synchronized camera views for broadcast from a live venue activity to remote viewers |
US20040032495A1 (en) * | 2000-10-26 | 2004-02-19 | Ortiz Luis M. | Providing multiple synchronized camera views for broadcast from a live venue activity to remote viewers |
US20020085092A1 (en) | 2000-11-14 | 2002-07-04 | Samsung Electronics Co., Ltd. | Object activity modeling method |
US20040131254A1 (en) * | 2000-11-24 | 2004-07-08 | Yiqing Liang | System and method for object identification and behavior characterization using video analysis |
US20020161804A1 (en) * | 2001-04-26 | 2002-10-31 | Patrick Chiu | Internet-based system for multimedia meeting minutes |
US20020178085A1 (en) * | 2001-05-15 | 2002-11-28 | Herb Sorensen | Purchase selection behavior analysis system and method |
US7006982B2 (en) | 2001-05-15 | 2006-02-28 | Sorensen Associates Inc. | Purchase selection behavior analysis system and method utilizing a visibility measure |
US20030053659A1 (en) * | 2001-06-29 | 2003-03-20 | Honeywell International Inc. | Moving object assessment system and method |
US20030002712A1 (en) * | 2001-07-02 | 2003-01-02 | Malcolm Steenburgh | Method and apparatus for measuring dwell time of objects in an environment |
US20030058339A1 (en) | 2001-09-27 | 2003-03-27 | Koninklijke Philips Electronics N.V. | Method and apparatus for detecting an event based on patterns of behavior |
US20030110038A1 (en) | 2001-10-16 | 2003-06-12 | Rajeev Sharma | Multi-modal gender classification using support vector machines (SVMs) |
US20040161133A1 (en) * | 2002-02-06 | 2004-08-19 | Avishai Elazar | System and method for video content analysis-based detection, surveillance and alarm management |
US20040120581A1 (en) | 2002-08-27 | 2004-06-24 | Ozer I. Burak | Method and apparatus for automated video activity analysis |
US20040113933A1 (en) | 2002-10-08 | 2004-06-17 | Northrop Grumman Corporation | Split and merge behavior analysis and understanding using Hidden Markov Models |
US20060010028A1 (en) * | 2003-11-14 | 2006-01-12 | Herb Sorensen | Video shopper tracking system and method |
US20050286774A1 (en) | 2004-06-28 | 2005-12-29 | Porikli Fatih M | Usual event detection in a video using object and frame features |
US20060010030A1 (en) * | 2004-07-09 | 2006-01-12 | Sorensen Associates Inc | System and method for modeling shopping behavior |
US20060023073A1 (en) * | 2004-07-27 | 2006-02-02 | Microsoft Corporation | System and method for interactive multi-view video |
US20060047674A1 (en) * | 2004-09-01 | 2006-03-02 | Mohammed Zubair Visharam | Method and apparatus for supporting storage of multiple camera views |
US20060053342A1 (en) | 2004-09-09 | 2006-03-09 | Bazakos Michael E | Unsupervised learning of events in a video sequence |
US20100002082A1 (en) * | 2005-03-25 | 2010-01-07 | Buehler Christopher J | Intelligent camera selection and object tracking |
US20060239645A1 (en) * | 2005-03-31 | 2006-10-26 | Honeywell International Inc. | Event packaged video sequence |
WO2006106496A1 (en) * | 2005-04-03 | 2006-10-12 | Nice Systems Ltd. | Apparatus and methods for the semi-automatic tracking and examining of an object or an event in a monitored site |
US20070208263A1 (en) * | 2006-03-01 | 2007-09-06 | Michael Sasha John | Systems and methods of medical monitoring according to patient state |
US20070250901A1 (en) * | 2006-03-30 | 2007-10-25 | Mcintire John P | Method and apparatus for annotating media streams |
US7623755B2 (en) * | 2006-08-17 | 2009-11-24 | Adobe Systems Incorporated | Techniques for positioning audio and video clips |
US8295597B1 (en) * | 2007-03-14 | 2012-10-23 | Videomining Corporation | Method and system for segmenting people in a physical space based on automatic behavior analysis |
US20110261172A1 (en) * | 2008-04-17 | 2011-10-27 | Terry Robert L | Stereoscopic viewer |
Non-Patent Citations (2)
Title |
---|
U.S. Appl. No. 60/808,283, Sharma, et al. |
U.S. Appl. No. 60/846,014, Sharma, et al. |
Cited By (90)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10590693B2 (en) * | 2008-07-18 | 2020-03-17 | Robert Osann, Jr. | Moving door system synchronized with pedestrians passing there-through |
US20170241185A1 (en) * | 2008-07-18 | 2017-08-24 | Robert Osann, Jr. | Moving door system synchronized with pedestrians passing there-through |
US11326387B2 (en) * | 2008-07-18 | 2022-05-10 | Robert Osann, Jr. | Automatic access control devices and clusters thereof |
US20120120201A1 (en) * | 2010-07-26 | 2012-05-17 | Matthew Ward | Method of integrating ad hoc camera networks in interactive mesh systems |
US11107122B2 (en) * | 2011-08-01 | 2021-08-31 | Verizon and Patent Licensing Inc. | Targeted advertisement content presentation methods and systems |
US20130036011A1 (en) * | 2011-08-01 | 2013-02-07 | Verizon Patent And Licensing, Inc. | Targeted Advertisement Content Presentation Methods and Systems |
US20130050496A1 (en) * | 2011-08-25 | 2013-02-28 | Electronics & Telecommunications Research Institute | Security monitoring method and apparatus using augmented reality |
US10070100B2 (en) * | 2011-09-22 | 2018-09-04 | Philips Lighting Holding B.V. | Imaging service using outdoor lighting networks |
US20140198216A1 (en) * | 2011-09-22 | 2014-07-17 | Koninklijke Philips N.V. | Imaging service using outdoor lighting networks |
US20130159460A1 (en) * | 2011-12-16 | 2013-06-20 | Mindshare Networks, Inc. | Harnessing naturally occurring characteristics of social networks |
US9137295B2 (en) * | 2011-12-16 | 2015-09-15 | Mindshare Networks | Determining audience engagement levels with presentations and providing content based on the engagement levels |
US9210300B2 (en) * | 2011-12-19 | 2015-12-08 | Nec Corporation | Time synchronization information computation device for synchronizing a plurality of videos, time synchronization information computation method for synchronizing a plurality of videos and time synchronization information computation program for synchronizing a plurality of videos |
US20140313413A1 (en) * | 2011-12-19 | 2014-10-23 | Nec Corporation | Time synchronization information computation device, time synchronization information computation method and time synchronization information computation program |
US9213902B2 (en) * | 2011-12-21 | 2015-12-15 | Universite Pierre Et Marie Curie (Paris 6) | Method of estimating optical flow on the basis of an asynchronous light sensor |
US20140363049A1 (en) * | 2011-12-21 | 2014-12-11 | Universite Pierre Et Marie Curie (Paris 6) | Method of estimating optical flow on the basis of an asynchronous light sensor |
US11170329B1 (en) | 2012-05-17 | 2021-11-09 | Catalina Marketing Corporation | System and method of initiating in-trip audits in a self-checkout system |
US10387817B2 (en) * | 2012-05-17 | 2019-08-20 | Catalina Marketing Corporation | System and method of initiating in-trip audits in a self-checkout system |
US20220058538A1 (en) * | 2012-05-17 | 2022-02-24 | Catalina Marketing Corporation | System and method of initiating in-trip audits in a self-checkout system |
US20130311230A1 (en) * | 2012-05-17 | 2013-11-21 | Catalina Marketing Corporation | System and method of initiating in-trip audits in a self-checkout system |
US20140063262A1 (en) * | 2012-08-31 | 2014-03-06 | Ncr Corporation | Techniques for checkout security using video surveillance |
US9311645B2 (en) * | 2012-08-31 | 2016-04-12 | Ncr Corporation | Techniques for checkout security using video surveillance |
US20160078286A1 (en) * | 2013-04-26 | 2016-03-17 | Nec Corporation | Monitoring device, monitoring method and monitoring program |
US9946921B2 (en) * | 2013-04-26 | 2018-04-17 | Nec Corporation | Monitoring device, monitoring method and monitoring program |
US11889009B2 (en) | 2013-07-26 | 2024-01-30 | Skybell Technologies Ip, Llc | Doorbell communication and electrical systems |
US11651665B2 (en) | 2013-07-26 | 2023-05-16 | Skybell Technologies Ip, Llc | Doorbell communities |
US11140253B2 (en) | 2013-07-26 | 2021-10-05 | Skybell Technologies Ip, Llc | Doorbell communication and electrical systems |
US11132877B2 (en) | 2013-07-26 | 2021-09-28 | Skybell Technologies Ip, Llc | Doorbell communities |
US11102027B2 (en) | 2013-07-26 | 2021-08-24 | Skybell Technologies Ip, Llc | Doorbell communication systems and methods |
US10204467B2 (en) | 2013-07-26 | 2019-02-12 | SkyBell Technologies, Inc. | Smart lock systems and methods |
US9736284B2 (en) | 2013-07-26 | 2017-08-15 | SkyBell Technologies, Inc. | Doorbell communication and electrical systems |
US11362853B2 (en) | 2013-07-26 | 2022-06-14 | Skybell Technologies Ip, Llc | Doorbell communication systems and methods |
US11386730B2 (en) | 2013-07-26 | 2022-07-12 | Skybell Technologies Ip, Llc | Smart lock systems and methods |
US10733823B2 (en) | 2013-07-26 | 2020-08-04 | Skybell Technologies Ip, Llc | Garage door communication systems and methods |
US10665072B1 (en) * | 2013-11-12 | 2020-05-26 | Kuna Systems Corporation | Sensor to characterize the behavior of a visitor or a notable event |
US9799183B2 (en) * | 2013-12-06 | 2017-10-24 | SkyBell Technologies, Inc. | Doorbell package detection systems and methods |
US9743049B2 (en) | 2013-12-06 | 2017-08-22 | SkyBell Technologies, Inc. | Doorbell communication systems and methods |
US9508239B1 (en) | 2013-12-06 | 2016-11-29 | SkyBell Technologies, Inc. | Doorbell package detection systems and methods |
US10462097B2 (en) * | 2013-12-16 | 2019-10-29 | Inbubbles Inc. | Space time region based communications |
US11140120B2 (en) * | 2013-12-16 | 2021-10-05 | Inbubbles Inc. | Space time region based communications |
US11615430B1 (en) * | 2014-02-05 | 2023-03-28 | Videomining Corporation | Method and system for measuring in-store location effectiveness based on shopper response and behavior analysis |
US11288606B2 (en) | 2014-02-14 | 2022-03-29 | Bby Solutions, Inc. | Wireless customer and labor management optimization in retail settings |
US10572843B2 (en) * | 2014-02-14 | 2020-02-25 | Bby Solutions, Inc. | Wireless customer and labor management optimization in retail settings |
US10649634B2 (en) * | 2014-06-06 | 2020-05-12 | International Business Machines Corporation | Indexing and annotating a usability test recording |
US20150356062A1 (en) * | 2014-06-06 | 2015-12-10 | International Business Machines Corporation | Indexing and annotating a usability test recording |
US11184589B2 (en) | 2014-06-23 | 2021-11-23 | Skybell Technologies Ip, Llc | Doorbell communication systems and methods |
US11343473B2 (en) | 2014-06-23 | 2022-05-24 | Skybell Technologies Ip, Llc | Doorbell communication systems and methods |
US9769435B2 (en) | 2014-08-11 | 2017-09-19 | SkyBell Technologies, Inc. | Monitoring systems and methods |
US20160078302A1 (en) * | 2014-09-11 | 2016-03-17 | Iomniscient Pty Ltd. | Image management system |
US9892325B2 (en) * | 2014-09-11 | 2018-02-13 | Iomniscient Pty Ltd | Image management system |
US10044519B2 (en) | 2015-01-05 | 2018-08-07 | SkyBell Technologies, Inc. | Doorbell communication systems and methods |
US9997036B2 (en) | 2015-02-17 | 2018-06-12 | SkyBell Technologies, Inc. | Power outlet cameras |
US11228739B2 (en) | 2015-03-07 | 2022-01-18 | Skybell Technologies Ip, Llc | Garage door communication systems and methods |
US11871155B2 (en) | 2015-03-07 | 2024-01-09 | Skybell Technologies Ip, Llc | Garage door communication systems and methods |
US11388373B2 (en) | 2015-03-07 | 2022-07-12 | Skybell Technologies Ip, Llc | Garage door communication systems and methods |
US11575537B2 (en) | 2015-03-27 | 2023-02-07 | Skybell Technologies Ip, Llc | Doorbell communication systems and methods |
US11381686B2 (en) | 2015-04-13 | 2022-07-05 | Skybell Technologies Ip, Llc | Power outlet cameras |
EP3311334A4 (en) * | 2015-06-18 | 2019-08-07 | Wizr | Cloud platform with multi camera synchronization |
JP2018534826A (en) * | 2015-09-23 | 2018-11-22 | ノキア テクノロジーズ オーユー | Select video content |
US11354683B1 (en) | 2015-12-30 | 2022-06-07 | Videomining Corporation | Method and system for creating anonymous shopper panel using multi-modal sensor fusion |
US10489660B2 (en) | 2016-01-21 | 2019-11-26 | Wizr Llc | Video processing with object identification |
EP3405889A4 (en) * | 2016-01-21 | 2019-08-28 | Wizr LLC | Cloud platform with multi camera synchronization |
US11361641B2 (en) | 2016-01-27 | 2022-06-14 | Skybell Technologies Ip, Llc | Doorbell package detection systems and methods |
US10262331B1 (en) | 2016-01-29 | 2019-04-16 | Videomining Corporation | Cross-channel in-store shopper behavior analysis |
US10963893B1 (en) | 2016-02-23 | 2021-03-30 | Videomining Corporation | Personalized decision tree based on in-store behavior analysis |
US10387896B1 (en) | 2016-04-27 | 2019-08-20 | Videomining Corporation | At-shelf brand strength tracking and decision analytics |
US10354262B1 (en) | 2016-06-02 | 2019-07-16 | Videomining Corporation | Brand-switching analysis using longitudinal tracking of at-shelf shopper behavior |
US11238401B1 (en) | 2017-03-27 | 2022-02-01 | Amazon Technologies, Inc. | Identifying user-item interactions in an automated facility |
US11887051B1 (en) | 2017-03-27 | 2024-01-30 | Amazon Technologies, Inc. | Identifying user-item interactions in an automated facility |
US11494729B1 (en) * | 2017-03-27 | 2022-11-08 | Amazon Technologies, Inc. | Identifying user-item interactions in an automated facility |
US11087271B1 (en) | 2017-03-27 | 2021-08-10 | Amazon Technologies, Inc. | Identifying user-item interactions in an automated facility |
US11810436B2 (en) | 2017-09-18 | 2023-11-07 | Skybell Technologies Ip, Llc | Outdoor security systems and methods |
US10909825B2 (en) | 2017-09-18 | 2021-02-02 | Skybell Technologies Ip, Llc | Outdoor security systems and methods |
US11651668B2 (en) | 2017-10-20 | 2023-05-16 | Skybell Technologies Ip, Llc | Doorbell communities |
US11210531B2 (en) * | 2018-08-20 | 2021-12-28 | Canon Kabushiki Kaisha | Information processing apparatus for presenting location to be observed, and method of the same |
US11074790B2 (en) | 2019-08-24 | 2021-07-27 | Skybell Technologies Ip, Llc | Doorbell communication systems and methods |
US11854376B2 (en) | 2019-08-24 | 2023-12-26 | Skybell Technologies Ip, Llc | Doorbell communication systems and methods |
US11380091B2 (en) | 2019-10-25 | 2022-07-05 | 7-Eleven, Inc. | System and method for populating a virtual shopping cart based on a verification of algorithmic determinations of items selected during a shopping session in a physical store |
US11017229B2 (en) | 2019-10-25 | 2021-05-25 | 7-Eleven, Inc. | System and method for selectively verifying algorithmically populated shopping carts |
US11023728B1 (en) | 2019-10-25 | 2021-06-01 | 7-Eleven, Inc. | Machine learning algorithm trained to identify algorithmically populated shopping carts as candidates for verification |
US11151388B2 (en) | 2019-10-25 | 2021-10-19 | 7-Eleven, Inc. | Customer-based video feed |
US11386647B2 (en) | 2019-10-25 | 2022-07-12 | 7-Eleven, Inc. | System and method for processing a refund request arising from a shopping session in a cashierless store |
US11475656B2 (en) | 2019-10-25 | 2022-10-18 | 7-Eleven, Inc. | System and method for selectively verifying algorithmically populated shopping carts |
US11475674B2 (en) | 2019-10-25 | 2022-10-18 | 7-Eleven, Inc. | Customer-based video feed |
US10922555B1 (en) * | 2019-10-25 | 2021-02-16 | 7-Eleven, Inc. | Customer-based video feed |
US11475657B2 (en) | 2019-10-25 | 2022-10-18 | 7-Eleven, Inc. | Machine learning algorithm trained to identify algorithmically populated shopping carts as candidates for verification |
US20220264053A1 (en) * | 2019-10-30 | 2022-08-18 | Beijing Bytedance Network Technology Co., Ltd. | Video processing method and device, terminal, and storage medium |
US11374808B2 (en) | 2020-05-29 | 2022-06-28 | Corning Research & Development Corporation | Automated logging of patching operations via mixed reality based labeling |
US11295135B2 (en) * | 2020-05-29 | 2022-04-05 | Corning Research & Development Corporation | Asset tracking of communication equipment via mixed reality based labeling |
US11682214B2 (en) * | 2021-10-05 | 2023-06-20 | Motorola Solutions, Inc. | Method, system and computer program product for reducing learning time for a newly installed camera |
US20230103735A1 (en) * | 2021-10-05 | 2023-04-06 | Motorola Solutions, Inc. | Method, system and computer program product for reducing learning time for a newly installed camera |
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